/usr/lib/python3/dist-packages/xarray/backends/common.py is in python3-xarray 0.10.2-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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import contextlib
import logging
import multiprocessing
import threading
import time
import traceback
import warnings
from collections import Mapping, OrderedDict
import numpy as np
from ..conventions import cf_encoder
from ..core import indexing
from ..core.pycompat import dask_array_type, iteritems
from ..core.utils import FrozenOrderedDict, NdimSizeLenMixin
# Import default lock
try:
from dask.utils import SerializableLock
HDF5_LOCK = SerializableLock()
except ImportError:
HDF5_LOCK = threading.Lock()
# Create a logger object, but don't add any handlers. Leave that to user code.
logger = logging.getLogger(__name__)
NONE_VAR_NAME = '__values__'
def get_scheduler(get=None, collection=None):
""" Determine the dask scheduler that is being used.
None is returned if not dask scheduler is active.
See also
--------
dask.utils.effective_get
"""
try:
from dask.utils import effective_get
actual_get = effective_get(get, collection)
try:
from dask.distributed import Client
if isinstance(actual_get.__self__, Client):
return 'distributed'
except (ImportError, AttributeError):
try:
import dask.multiprocessing
if actual_get == dask.multiprocessing.get:
return 'multiprocessing'
else:
return 'threaded'
except ImportError:
return 'threaded'
except ImportError:
return None
def get_scheduler_lock(scheduler, path_or_file=None):
""" Get the appropriate lock for a certain situation based onthe dask
scheduler used.
See Also
--------
dask.utils.get_scheduler_lock
"""
if scheduler == 'distributed':
from dask.distributed import Lock
return Lock(path_or_file)
elif scheduler == 'multiprocessing':
return multiprocessing.Lock()
elif scheduler == 'threaded':
from dask.utils import SerializableLock
return SerializableLock()
else:
return threading.Lock()
def _encode_variable_name(name):
if name is None:
name = NONE_VAR_NAME
return name
def _decode_variable_name(name):
if name == NONE_VAR_NAME:
name = None
return name
def find_root(ds):
"""
Helper function to find the root of a netcdf or h5netcdf dataset.
"""
while ds.parent is not None:
ds = ds.parent
return ds
def robust_getitem(array, key, catch=Exception, max_retries=6,
initial_delay=500):
"""
Robustly index an array, using retry logic with exponential backoff if any
of the errors ``catch`` are raised. The initial_delay is measured in ms.
With the default settings, the maximum delay will be in the range of 32-64
seconds.
"""
assert max_retries >= 0
for n in range(max_retries + 1):
try:
return array[key]
except catch:
if n == max_retries:
raise
base_delay = initial_delay * 2 ** n
next_delay = base_delay + np.random.randint(base_delay)
msg = ('getitem failed, waiting %s ms before trying again '
'(%s tries remaining). Full traceback: %s' %
(next_delay, max_retries - n, traceback.format_exc()))
logger.debug(msg)
time.sleep(1e-3 * next_delay)
class CombinedLock(object):
"""A combination of multiple locks.
Like a locked door, a CombinedLock is locked if any of its constituent
locks are locked.
"""
def __init__(self, locks):
self.locks = tuple(set(locks)) # remove duplicates
def acquire(self, *args):
return all(lock.acquire(*args) for lock in self.locks)
def release(self, *args):
for lock in self.locks:
lock.release(*args)
def __enter__(self):
for lock in self.locks:
lock.__enter__()
def __exit__(self, *args):
for lock in self.locks:
lock.__exit__(*args)
@property
def locked(self):
return any(lock.locked for lock in self.locks)
def __repr__(self):
return "CombinedLock(%r)" % list(self.locks)
class BackendArray(NdimSizeLenMixin, indexing.ExplicitlyIndexed):
def __array__(self, dtype=None):
key = indexing.BasicIndexer((slice(None),) * self.ndim)
return np.asarray(self[key], dtype=dtype)
class AbstractDataStore(Mapping):
_autoclose = None
_ds = None
_isopen = False
def __iter__(self):
return iter(self.variables)
def __getitem__(self, key):
return self.variables[key]
def __len__(self):
return len(self.variables)
def get_dimensions(self): # pragma: no cover
raise NotImplementedError
def get_attrs(self): # pragma: no cover
raise NotImplementedError
def get_variables(self): # pragma: no cover
raise NotImplementedError
def get_encoding(self):
return {}
def load(self):
"""
This loads the variables and attributes simultaneously.
A centralized loading function makes it easier to create
data stores that do automatic encoding/decoding.
For example:
class SuffixAppendingDataStore(AbstractDataStore):
def load(self):
variables, attributes = AbstractDataStore.load(self)
variables = {'%s_suffix' % k: v
for k, v in iteritems(variables)}
attributes = {'%s_suffix' % k: v
for k, v in iteritems(attributes)}
return variables, attributes
This function will be called anytime variables or attributes
are requested, so care should be taken to make sure its fast.
"""
variables = FrozenOrderedDict((_decode_variable_name(k), v)
for k, v in self.get_variables().items())
attributes = FrozenOrderedDict(self.get_attrs())
return variables, attributes
@property
def variables(self):
warnings.warn('The ``variables`` property has been deprecated and '
'will be removed in xarray v0.11.',
FutureWarning, stacklevel=2)
variables, _ = self.load()
return variables
@property
def attrs(self):
warnings.warn('The ``attrs`` property has been deprecated and '
'will be removed in xarray v0.11.',
FutureWarning, stacklevel=2)
_, attrs = self.load()
return attrs
@property
def dimensions(self):
warnings.warn('The ``dimensions`` property has been deprecated and '
'will be removed in xarray v0.11.',
FutureWarning, stacklevel=2)
return self.get_dimensions()
def close(self):
pass
def __enter__(self):
return self
def __exit__(self, exception_type, exception_value, traceback):
self.close()
class ArrayWriter(object):
def __init__(self, lock=HDF5_LOCK):
self.sources = []
self.targets = []
self.lock = lock
def add(self, source, target):
if isinstance(source, dask_array_type):
self.sources.append(source)
self.targets.append(target)
else:
target[...] = source
def sync(self):
if self.sources:
import dask.array as da
da.store(self.sources, self.targets, lock=self.lock)
self.sources = []
self.targets = []
class AbstractWritableDataStore(AbstractDataStore):
def __init__(self, writer=None, lock=HDF5_LOCK):
if writer is None:
writer = ArrayWriter(lock=lock)
self.writer = writer
def encode(self, variables, attributes):
"""
Encode the variables and attributes in this store
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
attributes : dict-like
Dictionary of key/value (attribute name / attribute) pairs
Returns
-------
variables : dict-like
attributes : dict-like
"""
variables = OrderedDict([(k, self.encode_variable(v))
for k, v in variables.items()])
attributes = OrderedDict([(k, self.encode_attribute(v))
for k, v in attributes.items()])
return variables, attributes
def encode_variable(self, v):
"""encode one variable"""
return v
def encode_attribute(self, a):
"""encode one attribute"""
return a
def set_dimension(self, d, l): # pragma: no cover
raise NotImplementedError
def set_attribute(self, k, v): # pragma: no cover
raise NotImplementedError
def set_variable(self, k, v): # pragma: no cover
raise NotImplementedError
def sync(self):
if self._isopen and self._autoclose:
# datastore will be reopened during write
self.close()
self.writer.sync()
def store_dataset(self, dataset):
"""
in stores, variables are all variables AND coordinates
in xarray.Dataset variables are variables NOT coordinates,
so here we pass the whole dataset in instead of doing
dataset.variables
"""
self.store(dataset, dataset.attrs)
def store(self, variables, attributes, check_encoding_set=frozenset(),
unlimited_dims=None):
"""
Top level method for putting data on this store, this method:
- encodes variables/attributes
- sets dimensions
- sets variables
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
attributes : dict-like
Dictionary of key/value (attribute name / attribute) pairs
check_encoding_set : list-like
List of variables that should be checked for invalid encoding
values
unlimited_dims : list-like
List of dimension names that should be treated as unlimited
dimensions.
"""
variables, attributes = self.encode(variables, attributes)
self.set_attributes(attributes)
self.set_dimensions(variables, unlimited_dims=unlimited_dims)
self.set_variables(variables, check_encoding_set,
unlimited_dims=unlimited_dims)
def set_attributes(self, attributes):
"""
This provides a centralized method to set the dataset attributes on the
data store.
Parameters
----------
attributes : dict-like
Dictionary of key/value (attribute name / attribute) pairs
"""
for k, v in iteritems(attributes):
self.set_attribute(k, v)
def set_variables(self, variables, check_encoding_set,
unlimited_dims=None):
"""
This provides a centralized method to set the variables on the data
store.
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
check_encoding_set : list-like
List of variables that should be checked for invalid encoding
values
unlimited_dims : list-like
List of dimension names that should be treated as unlimited
dimensions.
"""
for vn, v in iteritems(variables):
name = _encode_variable_name(vn)
check = vn in check_encoding_set
target, source = self.prepare_variable(
name, v, check, unlimited_dims=unlimited_dims)
self.writer.add(source, target)
def set_dimensions(self, variables, unlimited_dims=None):
"""
This provides a centralized method to set the dimensions on the data
store.
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
unlimited_dims : list-like
List of dimension names that should be treated as unlimited
dimensions.
"""
if unlimited_dims is None:
unlimited_dims = set()
existing_dims = self.get_dimensions()
dims = OrderedDict()
for v in unlimited_dims: # put unlimited_dims first
dims[v] = None
for v in variables.values():
dims.update(dict(zip(v.dims, v.shape)))
for dim, length in dims.items():
if dim in existing_dims and length != existing_dims[dim]:
raise ValueError(
"Unable to update size for existing dimension"
"%r (%d != %d)" % (dim, length, existing_dims[dim]))
elif dim not in existing_dims:
is_unlimited = dim in unlimited_dims
self.set_dimension(dim, length, is_unlimited)
class WritableCFDataStore(AbstractWritableDataStore):
def encode(self, variables, attributes):
# All NetCDF files get CF encoded by default, without this attempting
# to write times, for example, would fail.
variables, attributes = cf_encoder(variables, attributes)
variables = OrderedDict([(k, self.encode_variable(v))
for k, v in variables.items()])
attributes = OrderedDict([(k, self.encode_attribute(v))
for k, v in attributes.items()])
return variables, attributes
class DataStorePickleMixin(object):
"""Subclasses must define `ds`, `_opener` and `_mode` attributes.
Do not subclass this class: it is not part of xarray's external API.
"""
def __getstate__(self):
state = self.__dict__.copy()
del state['_ds']
del state['_isopen']
if self._mode == 'w':
# file has already been created, don't override when restoring
state['_mode'] = 'a'
return state
def __setstate__(self, state):
self.__dict__.update(state)
self._ds = None
self._isopen = False
@property
def ds(self):
if self._ds is not None and self._isopen:
return self._ds
ds = self._opener(mode=self._mode)
self._isopen = True
return ds
@contextlib.contextmanager
def ensure_open(self, autoclose=None):
"""
Helper function to make sure datasets are closed and opened
at appropriate times to avoid too many open file errors.
Use requires `autoclose=True` argument to `open_mfdataset`.
"""
if autoclose is None:
autoclose = self._autoclose
if not self._isopen:
try:
self._ds = self._opener()
self._isopen = True
yield
finally:
if autoclose:
self.close()
else:
yield
def assert_open(self):
if not self._isopen:
raise AssertionError('internal failure: file must be open '
'if `autoclose=True` is used.')
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